Using pre-segmentation with the adaptive cosine estimator and matched filter algorithms for hyperspectral target detection

Ori Feldman; Dor Klinman; Stanley R. Rotman

doi:10.1117/12.2585376

12 April 2021 Using pre-segmentation with the adaptive cosine estimator and matched filter algorithms for hyperspectral target detection

Ori Feldman, Dor Klinman, Stanley R. Rotman

Author Affiliations +

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 117270Y (2021) https://doi.org/10.1117/12.2585376
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Previous studies have shown that, for certain data sets, segmentation can help target detection performance for the Matched Filter (MF) algorithm. In this paper, we study the implementation of clustering prior to the Adaptive Cosine Estimator (ACE) calculation and compare our results to the classic non-segmented ACE and Matched Filter algorithms. From our results, we conclude that the proposed algorithm improves Matched Filter results in low false alarm rate conditions, achieving higher accuracy and lower false alarms in target detection; the ACE algorithm results are only marginally affected by segmentation.

Conference Presentation

Citation Download Citation

Ori Feldman, Dor Klinman, and Stanley R. Rotman "Using pre-segmentation with the adaptive cosine estimator and matched filter algorithms for hyperspectral target detection", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 117270Y (12 April 2021); https://doi.org/10.1117/12.2585376

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